# The MIT License (MIT)
#
# Copyright (c) 2021 NVIDIA CORPORATION
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
# the Software, and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

import torch
import torchvision.models as models
import argparse
import os

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--save", default="model.onnx")
    args = parser.parse_args()

    resnet50 = models.resnet50(pretrained=True)
    dummy_input = torch.randn(1, 3, 224, 224)
    resnet50 = resnet50.eval()

    torch.onnx.export(resnet50,
                      dummy_input,
                      args.save,
                      export_params=True,
                      opset_version=10,
                      do_constant_folding=True,
                      input_names=['input'],
                      output_names=['output'],
                      dynamic_axes={'input': {0: 'batch_size', 2: "height", 3: 'width'},
                                    'output': {0: 'batch_size'}})

    print("Saved {}".format(args.save))
